Unsupervised texture segmentation using Gabor filters
Pattern Recognition
The nature of statistical learning theory
The nature of statistical learning theory
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Combination Fingerprint Classifier
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Systematic Methods for the Computation of the Directional Fields and Singular Points of Fingerprints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Enhancing security and privacy in biometrics-based authentication systems
IBM Systems Journal - End-to-end security
Liveness Detection for Fingerprint Scanners Based on the Statistics of Wavelet Signal Processing
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Time-series detection of perspiration as a liveness test in fingerprint devices
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Filterbank-based fingerprint matching
IEEE Transactions on Image Processing
Classification of fingerprint images to real vs. spoof
International Journal of Biometrics
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This paper describes Gabor filter-based method to detect spoof fingerprint attacks in fingerprint biometric systems. It is based on the observation that, real and spoof fingerprints exhibit different textural characteristics. Textural measures based on Gabor energy and co-occurrence texture features are used to characterize fingerprint texture. Fingerprint image is filtered using a bank of four Gabor filters, and then a gray level co-occurrence matrix (GLCM) method is applied to filtered images to extract minute textural details. Dimensionality of the features is reduced by principal component analysis (PCA). We test features on three different classifiers: neural network, support vector machine and OneR; then we fuse all the classifiers using the "Max Rule" to form a hybrid classifier. Overall classification rates achieved with various classifiers range from ~94.12% to ~97.65%. Thus, the experimental results indicate that, the new liveness detection approach is a very promising technique, as it needs only one fingerprint and no extra hardware to detect vitality.